Customer Support RAG Chatbot
About
# Customer Support RAG Chatbot A Retrieval-Augmented Generation (RAG) chatbot trained on customer support documentation to assist users by answering queries and providing relevant support information. ## Features - Answers questions based on provided customer support documentation - Responds with "I don't know" for…
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- Answers questions based on provided customer support documentation
- Responds with "I don't know" for questions outside the documentation scope
- User-friendly web interface
- Semantic search for relevant information
- Context-aware responses
- Web scraping support for AngelOne documentation
- PDF processing for insurance documents
Setting up with Highlight
This MCP is not yet compatible with Highlight’s one-click setup. However, you can still use it with Highlight by following these steps:
- Download and install Highlight from highlightai.com/download
- Navigate to the plugins tab and select "Add Custom Plugin"
-
Configure the plugin with the settings below
Plugin Name
Customer Support RAG ChatbotCommand (node, npx, python, etc.)Please refer to the README for specific instructions on how to obtain API keys or other required environment variables.
- Enable "Start Automatically" if you want the plugin to start when Highlight launches
From the repository
1. Open your web browser and navigate to the deployed URL
2. Type your question in the chat interface
3. The chatbot will:
- Search the documentation for relevant information
- Generate a response based on the found information
- Respond with "I don't know" if the information is not in the documentation
1. Clone the repository:
git clone <repository-url>
cd <repository-name>
2. Create and activate a virtual environment:
```bash
python -m venv venv
To deploy the application:
1. Backend (FastAPI):
- Deploy to a cloud platform (e.g., Heroku, AWS, DigitalOcean)
- Set environment variables in the cloud platform
- Use a process manager (e.g., Gunicorn) to run the FastAPI server
2. Frontend (Streamlit):
- Deploy to Streamlit Cloud or similar platform
- Configure the frontend to point to your deployed backend URL
- Set environment variables in the deployment platform
Claude Desktop / Cursor
Paste into your MCP client config file to install this server.
{
"mcpServers": {
"customer support rag chatbot": {
"mcp-server-shantanu1711": {
"command": "python",
"args": [
"-m",
"venv",
"venv"
]
}
}
}
}McpServers
{
"mcp-server-shantanu1711": {
"command": "python",
"args": [
"-m",
"venv",
"venv"
]
}
}A Retrieval-Augmented Generation (RAG) chatbot trained on customer support documentation to assist users by answering queries and providing relevant support information.
Features
- Answers questions based on provided customer support documentation
- Responds with "I don't know" for questions outside the documentation scope
- User-friendly web interface
- Semantic search for relevant information
- Context-aware responses
- Web scraping support for AngelOne documentation
- PDF processing for insurance documents
Setup Instructions
1. Clone the repository:
git clone <repository-url>
cd <repository-name>
2. Create and activate a virtual environment:
```bash
python -m venv venv
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